Summary of Synthetic Dual Image Generation For Reduction Of Labeling Efforts in Semantic Segmentation Of Micrographs with a Customized Metric Function, by Matias Oscar Volman Stern et al.
Synthetic dual image generation for reduction of labeling efforts in semantic segmentation of micrographs with a customized metric function
by Matias Oscar Volman Stern, Dominic Hohs, Andreas Jansche, Timo Bernthaler, Gerhard Schneider
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a workflow for improving semantic segmentation models used in material analysis, specifically for micrographs. The authors address the issue of limited and imperfectly labeled data, which can lead to poor model performance. They propose generating synthetic microstructural images along with masks using a Vector Quantised-Variational AutoEncoder (VQ-VAE) and PixelCNN. The VQ-VAE is trained to generate discrete codes that can be used to sample new codes, which are then decoded by PixelCNN to produce synthetic images and masks. The authors evaluate the effectiveness of their approach by training U-Net models with varying amounts of synthetic data and real data, using a customized metric derived from mean Intersection over Union (mIoU) to assess performance. The proposed method reduces the need for sample preparation and acquisition times, making it a user-friendly solution for training semantic segmentation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways to make computers better at analyzing tiny pictures of materials. Right now, this process can be tricky because we don’t always have perfect labels for what’s in each picture. The authors came up with a way to create fake pictures and labels that can help train the computer models. They use special machines called Vector Quantised-Variational AutoEncoders (VQ-VAEs) and PixelCNNs to generate these fake pictures and labels. This allows them to test how well their methods work by training other computers to analyze real pictures using a combination of real and fake data. |
Keywords
* Artificial intelligence * Semantic segmentation * Synthetic data * Variational autoencoder
